Our world of 7 billion+ people is an unequal place. ‘Beauty’ is unequally distributed. ‘Intelligence’ is unequally distributed. Height is unequally distributed. Genetic advantages are unequally distributed. Against that uneven backdrop, is it any great wonder that something as volatile as ‘wealth‘ is also unequally distributed? In fact, are not all those other biological factors a form of wealth in their own right? We would contend that they are certainly of value. Why then this selective focus on purely material measures?

But let’s not debate the origins, or significance of that right now. For the sake of argument, let’s just accept it as a given, that we are considering material wealth ONLY and that DIFFERENCES in material wealth between people really matter – and that’s why the GAP between them matters. For convenience, let us also use the simple term ‘wealth’ to cover both a person’s accumulated assets AND their annual income from all sources. This saves us having to find an alternative to the rather useful (but somewhat misleading) GDP per capita data, traditionally used to measure wealth between people from around the world and from different periods of history. Agreed?

CAN WE AT LEAST AGREE DIFFERENCE IS A BAD THING?

Er, that could be tricky. Reading current literature on the subject seems to indicate that there is a ‘gap’ between ‘rich’ and ‘poor’ – and it’s getting wider. Let’s assume that analysis is correct. Instead of our own consolidated ‘wealth’ figure, we guess they are referring more to GDP/capita figures. Apart from maybe the top few thousand or so most-scrutinized individuals on the planet, we suspect that nobody has a detailed picture of what individual asset wealth really looks like across a country, let alone across the world’s population. So we are all working with averaged figures, sourced from various Government and/or UN statistics.

The analysis seems to suggest that there has long been a gap between the ‘wealth’ of the richest X% and the poorest Y%. No great surprise there, as we recognise that there is bound to be SOME difference, driven by all sorts of valid factors: amount of hours worked, amount of skill required, amount of risk involved, shrewdness of investment decisions, variable regional house price inflation and so on. In which case, the current heat of this ‘hot issue’ seems to be that this ‘gap’ is getting wider.

In summary then, difference itself is “acceptable”, but the AMOUNT of difference, or INCREASE in the difference perhaps IS NOT.

WHAT WOULD THE ‘RIGHT’ AMOUNT LOOK LIKE?

Nobody can credibly say, with any authority, what amount of difference is acceptable. Perhaps a reasonable best guess, removing approved variable factors, would be a ‘wealth’ distribution profile that looked rather like the ‘Normal Distribution Curve’. This curve is found again and again in the spread of values in nature.

You can take height, for example. If you plotted the exact height of all adult males (18+) in a given country, you would find that the spread of values followed an approximation to the Normal Distribution Curve of values, from the shortest to the tallest. While all sorts of variations can and will occur BETWEEN countries – from the lowest and highest values, to the values of the quartiles, means and medians – the shape of the curve itself will prove remarkably consistent. So we should not be surprised if ‘wealth’ follows a similar pattern.

SIMILAR, BUT DIFFERENT?

The way in which ‘wealth’ is distinctively different, is the RANGE in the spread of possible values. You will NOT find any person over 3 metres tall – and certainly not over 100 metres tall! Nor will you find a person zero metres tall – which is the notional equivalent of a person with no ‘wealth’. So with heightdifferences, we would be surprised to find the tallest person being more than, say TWICE the average for the population. Some might understandably look at that scale of difference in the NATURAL world, compare it to the degree of difference in the world of ‘wealth‘ and conclude that the sheer scale of ‘wealth’ differences are by direct implication, unnatural – and thus IMMORAL.

TWO SIMILAR ‘NATURAL’ EXTREMES

However, consider TWO counter arguments. First, consider how far a typical national adult population RUNS in the course of a year, on average. For the vast majority of the adult population in the developed world, the average for the year is unlikely to exceed much more than a few miles. Compare that ‘natural’ average to those who train for and run ultra-marathons. They are still human (we think), yet their average miles RUN in the course of the year will be hundreds – maybe even thousands of times the average for the rest of the population.

Secondly, consider gamblers. How many BIG WINNERS do you usually hear about? In Europe, we have the Euro Millions lottery, that just about ANYONE over 18 can enter. Every ticket supposedly stands an EQUAL chance of winning. All completely fair then, right? If you want to INCREASE your chance of winning, you just have to gamble more and buy more tickets. In the UK, there have been a few winners who made over US$100 million. Imagine that! Compare that to what the ‘average‘ person typically wins each year. Statistically, for the vast majority it will work out to be slightly less than they have spent on tickets in that same year. In some cases, it could be a LOT less. That’s how gambling works. You will find similar stories in Las Vegas. There WILL be the lucky few who make millions – sometimes even maybe hundreds of millions. The global Stock Markets are the same. If it was that predictable, we would ALL be making money at it. But it isn’t, so we don’t. Agreed?

WEALTH, EFFORT, RISK AND REWARD

Running your own business is a little more tricky to call. There are more things you can do to try to MAKE things go well, but you could still make one bad decision after 99 good ones – and end up going bankrupt. You may not notice ‘those’ business people so much. The ones who ‘fail’ in this way often have a tendency to take themselves out of the game. Once you have suffered a major financial setback, it is harder to find the financial and emotional capital, to enable you to try it all again – pitting yourself once more against the tough commercial competition out there. Some people just don’t have it in them. Nevertheless, many DO succeed – and a few succeed quite spectacularly. Yet even then, it never stops being something of a gamble. These then, are the ‘luckier‘ ones who top the scales at the upper end of our ‘wealth’ chart.

Because ‘wealth’ creation typically involves bothelements of ‘effort‘ (like our ultra-marathon runners) and ‘risk‘ (like our gamblers), we should not be so surprised that there are such significant VARIATIONS in the spread of ‘wealth’ values at the top end. Similarly, as there is nothing to stop human beings making ‘bad’decisions with their ‘wealth’, it should be no surprise to find some on our ‘wealth’ scale at-or-below zero. There is nothing intrinsically immoral, in our view, where variations in ‘wealth’ figures reflect differences in the contributing factors of effort and risk. That is even before we consider the morally-neutral, compound reinforcing factors of intelligence, inheritance, frugality, sacrifice, collaborative reward mechanisms and so on. We won’t even start on “being in the right place with the right face at the right time“.

IS THERE NO LIMIT TO EXCESS THEN?

We hope we have made our point. Significant DIFFERENCES in ‘wealth’ creation and accumulation can be explained with reference to morally-neutral factors. If those differences EXIST as a consequence of morally-neutral reasons, who is to say how wide a GAP is acceptable? This is a serious question – and before we go on, we want YOU to come up with an ANSWER you feel comfortable with. Ready?

So, if YOU accumulate WEALTH from morally-neutral means, then RETAIN that wealth, even though you possess TEN TIMES the wealth of the poorest person in YOUR respective community, is that MORAL? Or shall we measure it by multiples of the AVERAGE ‘wealth’ in your community? We’re serious, which figure will YOU choose to make immoral? Imagine that whatever YOU decide is going to be made into a brand new Law, enforceable by imprisonment. Ten times the average sounds pretty excessive, right?

So let’s just consider that choice for a moment. In the UK, there is Minimum Wage legislation, that basically means employers cannot pay an adult over 21 years old, less than about US$10 per hour, except in some special circumstances. Assuming a working week of 40 hours, that’s US$400 a week, or around US$20,000 per year. Nice and easy numbers for our calculations. So then, using our simple ‘wealth’ measure and our chosen tenfold cap rule, nobody should be ALLOWED to create more than US$200k per year, if we were to use the ‘Minimum’ measures as our guide. Agreed? Does that still seem FAIR to you, to penalise those who earn more than that with imprisonment?

But, you may rightly point out, that is the MNIMUM wage. What’s the AVERAGE wage? Well, since you ask, it is considered to be around US$40,000, or about TWICE the Minimum Wage calculation figure [Note: UK average GDP/capita figure using ‘PPP’, is around $36k by comparison]. So, sticking with the multiplier of 10, shall we still imprison those earning US$400k per year or more? If so, you don’t want to be a film or pop star – any significant success will land them ALL behind bars!

CAN CLEARER MINDS PREVAIL?

We hope that this light-hearted example makes the very SERIOUS point. Some people can and will earn more thanten times the AVERAGE income figure, even in more developed economies. The UK is considered one of the world’s top 5 economies (6th in 2012). Rich by both world and most people’s standards. Yet it is not so very unusual for some of the highest earners to make even 100 timesthe UK’s average personal income each year. Much (but we cannot guarantee all) of that will be via morally-neutral means.

So now back to you for a moment. What was the multiple YOU chose, beyond which you felt that the level of income being earned was somehow not morally ‘right’? In developed economies, people are NOT actually thrown into to the penal system for being high earners, but they ARE penalised. There is typically a penalty to pay for being a higher income earner – and it comes in the form of TAXATION.

TAXATION: A MORAL SURROGATE?

Taxation is itself a system with its flaws and holes, but it is intended to take more from higher earners to invest in services that all citizens can benefit from. Or alternatively, those taxes are used to reduce the overall tax burden on lower earners. In that way, lower earners are meant to SHARE in the overall benefit of the earnings of the higher earners, albeit indirectly. This is intended to be an attempt at ‘fairness’, in that those earning more, pay a greater portion of their highest levels of income in taxes.

In the UK for example, the current top income tax level is 45% of income above around US$250k per year. So almost half of all income above that point is meant to go in taxes. Hence, although the headline income figure can be high, the take-home income figure can be just above half of that. And it is the take home figure that counts – as that is the bit you can actually spend.

DRAWING A MORAL LINE

Drawing a ‘moral’ line at any given income point is potentially fraught with difficulties and ambiguities – not to mention a fair amount of hypocrisy. If one ‘caps’ citizen income per year at say 10 times the national average, then does 9.9 times still count as moral? Also, does that ‘moral compass’ extend beyond your national borders globally? For example, Somalia’s average GDP per capita is estimated at around US$600. Should we then cap international earnings at US$6000 – or even US$60,000, if we allow an upper limit multiple of 100 times the Somalian figure?

Our point is, if you decide it is IMMORAL for one person to earn 10, or even 100 times what the average income is in ONE country, why not extend that same principle to other less privileged countries all around you? Why should that level of DIFFERENCE in ‘wealth’ be immoral WITHIN a country, but not BETWEEN countries? Our view is that trying to claim that any AMOUNTS of relative ‘wealth’ creation is intrinsically immoral – is futile.

We see no obvious immoral amounts, just potentially immoral MEANS. Any moral focus is more fruitfully directed to what to DO with ‘wealth’ AFTER it has been accumulated. And when it comes to such MORAL arguments over ‘wealth’, we feel it is best if people FIRST try to ‘step into the other person’s shoes’. Before attempting to make any such judgements, you must first seek to imagine what YOU would be willing to do if you were in ‘their’ position. Because for SOME of the world’s ultra-poor, you ARE precisely in ‘their’ position.

ARE YOU TURNING HEADS IN THE GLOBAL ‘RICH LIST’?

We like to use the idea of the world’s 7 billion+ people, all ranked in ‘wealth’ order. The ‘wealthiest’ at the front of the queue (obviously), the ‘poorest’ at the back. Again, a reminder that we are choosing to use that very simplistic view of ‘wealth’, based on basic GDP/capita type figures. So where are you going to put yourself? Even among the so-called ‘middle-classes’ in more developed nations, there can be a tendency to look ‘ahead’ at those wealthier still, wondering why THEY are not doing more to help ‘the poor’.

Despite average annual wages in the UK of around US$40k per year, with a figure of almost 30 million in some kind of employment, those near the Minimum Wage will still tend to think of themselves as relatively poor. After all, the very phrase ‘minimum wage’ implies they are at the very bottom of the imaginary ‘pile’. Butconsidered alongside the ‘average’ Somalian, perspectives can change. Could millions of Somalians be looking at the UK’s minimum wage earners and wondering why they are not doing more to overcome poverty for Somalians? After all, there are a LOT more of them than the UK’s richest elite.

Yet those same UK minimum wagers are looking further up the notional queue, towards others in their own nation making a million dollars a year or more – with both envy and perhaps a sense of moral outrage. Who needs that kind of money anyway? Even people earning those millions could, with the same kind of forward-blinkered view, be looking at the world’s billionaires and wondering why THEY don’t do more. And so it could go on. When one person has a billion dollars and another has 50 times as much, even the former could believe the latter is the one who should be “doing more for the poor”. After all, THEY can afford it.

THINK YOURSELF RICH!

And so we return to the Poverty Gap that we believe will never be closed. There will always be DIFFERENCE in the wealth earned and owned between individuals. If you have difference, then some will always be ‘richer’, while others will always be ‘poorer’. In THAT sense, it is true: “The poor you will always have with you“, as Jesus is quoted as saying.

Our concern is that those best-placedto act against the worst extremes of povertyshould be encouraged to do so. This does NOT mean the richest – but all those with the power, the opportunity and the inclination to act. THOSE are the people we hope to co-ordinate into a ‘coalition of the willing’.

Why spend ourselves and our energies trying to persuade a limited group of theoretical ‘OTHERS’ to act on closing a notional poverty gap, when WE are already (apparently) so fired up to take action OURSELVES? Better the few with a willing heart, in our view. That is more likely to build an enthusiastic momentum among the masses – a billion of us, we hope.

We regularly have visitors to this site from over 125 COUNTRIES, collectively representing over 90% of the world’s population – so you are already in good company. We welcome all those best-placed to act, among this coalition of the willing. Inform yourself – educate others. This is our way. The do-ers do. The don’t-ers don’t.

If a person is more closely bound to keeping a billion dollars they don’t need, than helping a billion people who can’t read – they have become poor in a way that you won’t find in any dictionary.

If you are one of the many globally who wants to be more effective in closing the extreme povertygap – then we have higher hopes for you.

Our condensed 7 word plan for solving poverty is: define poverty; map poverty; focus the fixers(6 words if you leave out “the”). We have written elsewhere an introduction to the third step of ‘focusing fixers’. You may find it helpful to read that article before you delve into the detail of this one. Here, we plan to expand on how fixers can focus more on outcome impacts, through the effective mechanism of MEASUREMENT.

The 7 Layer Poverty Model takes advantage of a specific definition of poverty, that we can use effectively for measurement. Our proposal, in the absence of some better tool, is to use the metrics from Simple Assessment Studies or estimates, to provide an approximation to the overall impactof any course of action, or poverty reduction initiative. This is a useful approach for comparing and choosing between investment decisions BEFORE any commitment of resources has been made. The same tool can be used to conduct a separate Simple Assessment after the particular project, allowing decision-makers to assess the ACTUAL impact, versus the PREDICTED impact.

A COMMON GLOBAL MODEL FOR DECISION-MAKING?

We are going to stick our necks out here. Almost all decisions made by any group anywhere in the world ever, have 3 KEY COMMON COMPONENTS. Like we said – necks out. All decision-making can be better understood by gaining a better idea of how those 3 components operate and affect the resulting DECISIONS. We are particularly interested here in how those decisions help us solve poverty, but they apply equally to all types of decision. They are:

The Decision-Making Unit (DMU)

The Decision-Making Process (DMP); and

The Basis of Decision (BOD).

The DMU can be anything from an individual, through to the entire national voting mechanism of the World’s largest democracy (India), which is currently under way in April 2014, with over 800 million people potentially involved (ie. the ‘electorate’). The ‘decision’ in this latter case, is who should be elected into national political power for the next term in office. But at the other end of the scale, the decisions the individual makes can be as simple as ‘shall I have a coffee now?’ Both the individual and 800+ million Indian voters constitute ‘decision-making units’, for those different scenarios. Thankfully, we don’t need to ask 800 million people every time we want make a decision about a cup of coffee. Imagine the wait in line at Starbucks!

And this is partly our point. We give various decision-making tasks to various different groups, of all shapes, sizes and memberships – but they are all DMU’s. There is no single PROCESS for all such decisions that they make, but they still all FOLLOW a process – however, haphazard and obscure such processes may sometimes seem, or indeed be. Some people may make serious life decisions based on horoscopes, or a throw of the dice. Elsewhere, the decision-making process may be by such things as secret ballots, canvassing opinions, a ‘show of hands’, or it may just be that whoever is considered the overall leader has to make the final decision – as will often be the case in military decisions and organisations. At the other end, you may have made some of your own decisions on the toss of a coin, or by trusting a “gut feeling“. In all cases, there was a process. The same is also true of our fixers, when it comes to making their decisions about tackling poverty.

Countless decisions, by government politicians, NGO Boards, multilateral Councils and their various equivalents, are made around the world, in the common pursuit of solving poverty – always following their respective processes. In many cases, the process will include the presenting of information, before the relevant members of the DMU assess that information (through internal or external discussion and debate) and then move towards a decision. That decision may be put to some kind of majority vote, left to a single individual, steered towards or consensus, or deferred. However, even a decision to defer a decision is also a decision itself. Such DMP approaches have been used worldwide for centuries – even millennia. But what of the flawed and fallible people who MAKE those decisions?

Their individual BEHAVIOUR is determined by the third item on our list: the Basis of Decision. The BOD is perhaps the hardest element to work out, for each individual involved. We recognise that information of some sort will typically have been shared, before any decision is sought. However, it would be wrong to assume that all decisions are made on the basis of the content of that information alone. There may be all kinds of other factors influencing the BOD of each member of the DMU – and each individual’s BOD may itself be different – sometimes significantly so.

A simple illustration would be the example of a bribe. If a member of the International Olympic Committee has to vote on the selection of the next City venue for the Olympics, it is conceivable that they may make their decision based on the expectation of a bribe – rather than the specific merits of any given city, or the information presented about it. (We neither confirm nor deny such ideas; we merely state that they are conceivable). In other circumstances, individuals in decision-making meetings may have some less obvious vested interest in a particular outcome. They may also simply dislike the person making the proposal, so will vote against the ideas based on that negative feeling, rather than a lack of compelling information. On a more positive note, people may be inclined to vote based on conscience, faith, practical limitations, personal fears, differing risk assessments, or just plain strength of “gut feeling”. They may not even be able to explain to you exactly WHAT their BOD was, after they have cast their vote. As human beings, we regularly have to operate on limited information – and even the information we do have may itself be misleading.

One of the areas where this is currently globally evident is around the complex area of global climate change. In this arena, we find lots of passionate debate, by various experts, around what the likely future climate scenarios will be and the best ways to respond to them. Gathering the ‘fixers’ to solve poverty can sometimes appear equally challenging. However for poverty, there is no single, internationally recognised group, interfacing as a ‘boundary organisation’ between decision-makers and differing expert opinions, with the overall brief to arrive at a consensus. For climate change, there is the IPCC (Intergovernmental Panel for Climate Change). For poverty, which is literally killing millions more every year than climate change, there is no such consensus body. Even if there was this single DMU, it would clearly not replace the many other DMUs, DMPs and BODs that would still continue to operate around the world every day.

A MORE STRUCTURED APPROACH TO DECISION-MAKING

So how can we FOCUS all the disparate groups collectively involved in overcoming poverty better? We have no plan for changing their DMUs, but would welcome the global equivalent of the IPCC for poverty. We have no alternative model for their respective decision-making processes either. The KEY area where we advocate improvement, is in their respective BODs. This may in turn influence change in the other 2 key decision-making elements, but that is not specifically our agenda here. We firmly believe that better quality information will facilitate better quality decision-making overall. The 7 Layer Poverty Model provides a compelling conceptual framework for a better understanding of what poverty is and how it can be overcome more effectively in a co-ordinated way.

We have proposed and included below an Excel spreadsheet, that incorporates certain ideas for focusing fixers. ‘Ifyou want to improve in any area, measure it more frequently’. People tend to focus more on what they think they will be measured on. Regarding improving the BOD on any and all decisionsbeing made with respect to solving poverty, we want to focus attention on measuring output impacts for given project ‘inputs’. These range from your own individual decisions about where you invest your own time, efforts, energies, attention and resources – to where the UN and the European Union decide to invest theirs. We believe that the final BOD on behalf of all public and charitable bodies can be much clearer and more transparent than at present – and we want to help make that transition a little easier.

We have come up with 25 useful things that the spreadsheet above helps bring out. There are more, but these will do for now. We suggest you download the spreadsheet and ideally have it open (or a copy of it) so you can cross check each point we make.

The first column is entitled ‘Item Ref’, as any user of the sheet may wish to allocate their own unique internal project code, or identifier. Each separate line of the sheet may be allocated to parts of an integrated project, that may have different kinds of impact benefits in different layers of the 7 Layer Poverty Model. An example might be a village water project, intended to have drinking water, health and sanitation (shelter) benefits, all from the one project. This can be represented as 1 cost line and multiple benefit lines, or the overall cost may be divided between the multiple benefit lines.

The Poverty Model category relates to the most relevant layer of the 7 Layer Poverty Model, that any project or initiative fits into.

The list of Project and Program types will be a list that is most relevant to the organisation using the sheet. We have included a range of visible and varied types, which are evident in public literature on solving poverty. Our short list of 21 items illustrates just how varied those poverty reduction initiatives can be.

We have shown project cost in thousands of US dollars, as a recognised international currency standard. This is perhaps easier for international sharing. Clearly, the currency header can be changed, along with later calculation columns using the cost figures.

The next 3 columns deal with measures for Importance, Urgency and Ease. These are intended to relate to these factors in the destination location for the activity, meaning how urgent the felt need is ‘on the ground’. We have proposed a system where 1 is the top priority, so the smaller the number, the greater the priority. It could equally be used the other way around, such that the higher the number, the greater the priority. It is the user’s choice. Each organisation must determine its own set of criteria for determining importance, urgency and ease. Importance may be driven by assessed impacts on lives, health and quality of life measures for estimated total populations. Urgency will typically reflect the relative effects of certain inflexible time pressures, such as drought season dates, harvest times and time to exhaustion of current key supplies at current consumption rates. Ease will reflect multiple compound conditions that all operationally help or hinder activities.

The figures for urgency, importance and ease are then MULTIPLIED in our system. This increases the effect on the overall scale of possible resulting numbers. It is intended to reflect the compound benefits and prioritisation of things which are important, urgent and easy to fix. These would typically commend themselves as top priorities on these metrics alone – in terms of operational project desirability. The ‘ease’ measure only reflects in-location operational constraints, rather than other wider potential constraints.

All the columns have Excel Filters activated, to enable the user to show just the rows of a given type, or ranked high-to-low, or low-to-high. This enables quick and easy cross comparisons between projects on different rows.

The right-hand side of the sheet comes on to deal with IMPACTS. In the absence of some better measures, we propose the number of people impacted is a key measure. Note, we have only assumed positive impacts, but recognise that some may need to reflect potentially negative impacts too and will need to amend this basic template accordingly. The number of people in all cases will need to be a best estimate, rather than no estimate at all. Assumptions which any estimate is based upon can be recorded as notes on the sheet as necessary.

The people number estimates are divided into ‘first year’ and ‘lifetime’, recognising that various organisations may need to show progress and results in different timeframes. Projects may need to be broken down into phases, such as construction and maintenance phases, for example. The different time periods allow decision-makers to make comparisons between possible short term goals and long term objectives.

The ‘average model score increase’ may be different between the first year and lifetime of a project. In all cases, an average assessment may be challenging, but the aim here is the ‘best available data’, rather than ‘no data at all’. When projects are undertaken, there is a reasonable expectation that certain individuals will benefit in certain ways. These should be measurable in most cases by the Simple Assessment method. Otherwise, some alternative key performance indicatorestimate will need to be used. The average score is suggested, as some may benefit more than others. For example, a new water pump will benefit more remote households less than those in its immediate vicinity.

The numbers of impacted people are then multiplied by the average model improvement impacts, to give a total year 1 score and a lifetime score. The significance of appropriately measuring the lifetime scores in terms of benefits becomes apparent. This may assist with an understandable tendency towards short-termism in certain scenarios. For example, those with elections coming up may be pressing for quick progress on certain key publicity projects, while there may be significantly greater benefit for projects that deliver long after the elections are decided.

In the absence of some better measure, we propose the overall ‘Model Improvement’ per US$1000. This might equally be any other relevant currency unit, or unit scale of measure, depending on the needs of the assessment. A village will clearly be looking on a different scale than a nation, but they can still both use the same common principles of impact measurement.

The 2 yellow shaded columns relate to INTERNAL factors within the organisation. The last column might be used to identify various project stages, such as: under consideration, approved, under way, maintenance phase, completed and so on.

The other yellow column is an internal weighting factor. There may be various things that determine this number, which are organisation-specific. They may relate to the kinds of things sponsors are particularly pressing for, or there may be reasons that the organisation wants to make a particular project type a higher priority – such as a proof-of-concept, or pilot study projects. There may also be external factors too. Action Aid in the UK has a commitment from the UK government to MATCH any donations given by a specific date this year, specifically related to a gender equality initiative. Hence, they may weight the programme more heavily up until that date, as effectively half their associated costs would be externally matched. The single weighting factor allows for a consolidated, overall internal assessment of how different projects might suit different organisations. The “impact” figure that results may simply be used for internal decision-making purposes.

There is quite a dramatic difference in the impacts of certain initiatives when measured this way. A certain amount of ‘sanity-checking’ between impact estimates and other projects is wise. Any significant anomalies can be referenced and explained in notes within the sheet itself.

One of the things that may be identified, is where certain projects may NOT be to an organisation’s strengths, but could have significant impact increases, if completed by a collaboration partner. For example, when building wells, one organisation may be set up for construction. Another might be set up for re-training local farm labourers as mechanics. The former might share the benefit of the ‘lifetime impact’ figures with another organisation, which can train the engineers to keep the water pumps repaired. This way, significant lifetime benefit increases can be shown with minimal increase in costs. This highlights the multiplied benefits of collaboration between organisations, when overcoming widespread poverty.

Note that the internal weighting factor we used worked on a scale of 0-10. This permitted fractions, where a given project was not in an organisation’s core skills to deliver.

The types of projects listed range from direct local action to high-level political advocacy. Both have their merits. At the higher levels, where multiple organisations may be conducting advocacy, co-ordination between those organisations on the commonly-agreed benefit estimate would be more compelling than a diverse range of estimates. By contrast, we have included ‘Shoes Project #1’ as an example of the “bright idea” project, where organisations think of something that they intend to capture the public imagination. In this case, the idea was to send 1000 pairs of shoes from Europe to the poor in Africa. In practice, the project would be very expensive and time-consuming, yielding little actual benefit. However, it might be the kind of thing that captures media attention and free publicity for more impactful projects.

The model improvements are calculated by working out theoretical (but possible) numbers for the relevant individuals on the relevant sections of the Poverty Profile of the individual. This is all condensed into a single number for calculation purposes. In the average model score increase for year 1. within the cell, the component calculation is shown. It is possible that the calculations happen on a different sheet (eg a Simple Assessment source sheet) and the resulting Poverty Profile numbers can then be fed directly into this sheet, for impact calculations. This helps reviewers understand the origins of the different impact measure calculations.

The significant impact of the ‘zero’ effect on overall totals, can be seen with the Africa Water #1 project. In the imagined scenario, the availability and accessibility of the community water supply were already ‘high’, but the pollution of the existing supply required an urgent newly-drilled replacement well. This turned a zero figure into the maximum 27 figure on the impact assessment model improvement. This highlights the potential for certain ‘quick-wins’, with dramatic potential improvements possible in certain situations. This scoring system will help highlight such projects and so help motivate fixers to take swift and effective action on them.

The difference between certain projects can be well-illustrated using this approach, The cost of building a primary school is illustrated Africa Primary School #2. It’s impact can be measured directly by how many pupils can be expected to attend that school. However, many of those pupils might still attend ANOTHER school, of that new school wasn’t built. So the new school impact should really be measured in terms of convenience and NET increase in school attendance numbers. School projects may be popular with locals, as it can directly save them significant school costs per child. In that respect, the project is changing the accessibility to the Engagement aspect of primary school education, rather than its availability, or its attributes necessarily.

In terms of child sponsorship, it may be that the ability to purchase uniforms and materials is the key requirement, having the biggest net impact on local school attendance. Having a measurement system like this gives a more analytical way to compare overall positive impacts per donor dollar, for different kinds of solving poverty ideas. Providing transport to a school and child sponsorship within it may prove more cost effective than building a new school, for example. Only accurate assessment based on informed local insight can tell.

It is recognised that the higher levels of the Humanitarian Basics can be more challenging to quantify, in terms of impacts. The example of advocacy for freedom of the press in Civil Liberties #3 illustrates this, The number of people impacted over the lifetime of such a project could be very high, but there is also a risk component attached. The lobbying may take place and no change could occur. This could be reflected in the internal weighting measure, Such higher risk projects may be better shared with other organisations, who also share the costs.

Remarkable claims should ideally be backed by remarkable levels of evidence to substantiate them. Hence, if the number of people impacted in a project lifetime runs into thousands or millions, then clearly decision-makers will want to have such claims backed up by more rigorous research data. It may even merit a pilot project to further back up the claims in practice.

The columns, calculations and project examples are our own. The 3 decision drivers of importance, urgency and ease of execution have been tried and tested. The application of the Poverty Model improvement measures is new. Our purpose is to give decision-makers a flexible tool and an idea of how to use it to arrive at better, more structured and more consistent decision-making. Users are free to adapt it to come up with better versions themselves. The common goal is to help focus fixers on maximising their output impacts, in the interest of overcoming more poverty sooner.

We trust that you find this examination of our basic template useful. We would be interested to hear of helpful experiences of using it in practice in solving poverty. As ever, if you have better ideas on how to better focus the poverty fixers, we would love to hear them.

Not necessarily. When it comes to measuring, understanding & overcoming poverty, we want to avoid oversimplifying what can often be truly complex issues. However, we also want to ensure that we DO simplify things to the point where MOST people can understand them. That’s why we always want to keep in mind the idea of a 5 year old child discussing poverty issues with their friends. If we can put things into terms that they can understand, then we figure the rest of the world should be able to follow right along. That’s where the 7 Layer Poverty Model comes in.

BETTER TO DUMB DOWN THAN SWITCH OFF?

When it comes to communicating a potentially complex idea like poverty, we realise that people can switch off quite quickly if they don’t follow you. The precedent for us here is the message of “5 a day” regarding the nutritional value of fruit and vegetables in the regular human diet. How many years did experts understand the benefits of more fruit and veg in our diets, but struggled to get their important message heard, until “5 a day” came along?

We know it is more complicated than that underneath the surface, but at least it is simple enough that kids will talk about it and multiple stakeholders in the issue (governments, health organisations, produce suppliers and parents) can get behind it. So let’s not kill it with complexity! There may well be a case for adding complexity to the 7 Layer Poverty Model with subsequent enhancements, or bespoke applicationsto a specific set of circumstances. However, for the sake of the broadest possible understanding & adoption among the global stakeholders in overcoming poverty – let’s keep it simple for now. Agreed?

UNDERSTANDING IS COMMUNICATION’S PRIMARY GOAL

After all, who wants to develop a model that is so complex that nobody else understands it? You may achieve individual insight, but not collective understanding. And for now, collective understanding is our more pressing goal. And we have a billion people to reach. Lots of different languages to consider too. We believe that we will all overcome far more poverty, far more quickly and with far fewer resources, if we CO-ORDINATE our present efforts more effectively. To improve co-ordination, we must first improve understanding. To improve understanding, we must establish a commonPoverty Model that is powerful enough that most stakeholders can use it, yet simple enough that most kids can understand it.